The invention relates to a coronary 
artery sequence 
blood vessel segmentation method based on space-time discriminative 
feature learning, which is used for carrying out 
blood vessel segmentation 
processing on a cardiac coronary 
artery angiography sequence image, and includes 
processing a current frame of image and several adjacent frames of images based on a pre-trained improved Unet 
network model, and obtaining 
blood vessel segmentation result of current frame image, wherein the improved Unet 
network model comprises a coding part, a jump connection layer and a decoding part, the coding part adopts a 3D 
convolution layer to perform time-space 
feature extraction, the decoding part is provided with a channel attention module, and the jump connection layer aggregates features extracted by thecoding part, thus obtaining an aggregation feature map and transmitting the aggregation feature map to the decoding part. Compared with the prior art, the cardiac coronary 
artery blood 
vessel segmentation method introduces the spatial-temporal features to perform cardiac coronary artery blood 
vessel segmentation, reduces the interference of 
time domain noise, emphasizes the blood vessel features,alleviates the problem of 
class imbalance in blood 
vessel segmentation, and has higher blood vessel segmentation accuracy.